LDAP and Databricks Integration

Input and output integration overview

The LDAP plugin collects monitoring metrics from LDAP servers, including OpenLDAP and 389 Directory Server. This plugin is essential for tracking the performance and health of LDAP services, enabling administrators to gain insights into their directory operations.

Use Telegraf’s HTTP output plugin to push metrics straight into a Databricks Lakehouse by calling the SQL Statement Execution API with a JSON-wrapped INSERT or volume PUT command.

Integration details

LDAP

This plugin gathers metrics from LDAP servers’ monitoring backend, specifically from the cn=Monitor entries. It supports two prominent LDAP implementations: OpenLDAP and 389 Directory Server (389ds). With a focus on collecting various operational metrics, the LDAP plugin enables administrators to monitor performance, connection status, and server health in real-time, which is vital for maintaining robust directory services. By allowing customizable connection parameters and security configurations, such as TLS support, the plugin ensures compliance with best practices for security and performance. Metrics gathered can be instrumental in identifying trends, optimizing server configurations, and enforcing service-level agreements with stakeholders.

Databricks

This configuration turns Telegraf into a lightweight ingestion agent for the Databricks Lakehouse. It leverages the Databricks SQL Statement Execution API 2.0, which accepts authenticated POST requests containing a JSON payload with a statement field. Each Telegraf flush dynamically renders a SQL INSERT (or, for file-based workflows, a PUT ... INTO /Volumes/... command) that lands the metrics into a Unity Catalog table or volume governed by Lakehouse security. Under the hood Databricks stores successful inserts as Delta Lake transactions, enabling ACID guarantees, time-travel, and scalable analytics. Operators can point the warehouse_id at any serverless or classic SQL warehouse, and all authentication is handled with a PAT or service-principal token—no agents or JDBC drivers required. Because Telegraf’s HTTP output supports custom headers, batching, TLS, and proxy settings, the same pattern scales from edge IoT gateways to container sidecars, consolidating infrastructure telemetry, application logs, or business KPIs directly into the Lakehouse for BI, ML, and Lakehouse Monitoring. Unity Catalog volumes provide a governed staging layer when file uploads and COPY INTO are preferred, and the approach aligns with Databricks’ recommended ingestion practices for partners and ISVs.

Configuration

LDAP

[[inputs.ldap]]
  ## Server to monitor
  ## The scheme determines the mode to use for connection with
  ##    ldap://...      -- unencrypted (non-TLS) connection
  ##    ldaps://...     -- TLS connection
  ##    starttls://...  --  StartTLS connection
  ## If no port is given, the default ports, 389 for ldap and starttls and
  ## 636 for ldaps, are used.
  server = "ldap://localhost"

  ## Server dialect, can be "openldap" or "389ds"
  # dialect = "openldap"

  # DN and password to bind with
  ## If bind_dn is empty an anonymous bind is performed.
  bind_dn = ""
  bind_password = ""

  ## Reverse the field names constructed from the monitoring DN
  # reverse_field_names = false

  ## Optional TLS Config
  ## Set to true/false to enforce TLS being enabled/disabled. If not set,
  ## enable TLS only if any of the other options are specified.
  # tls_enable =
  ## Trusted root certificates for server
  # tls_ca = "/path/to/cafile"
  ## Used for TLS client certificate authentication
  # tls_cert = "/path/to/certfile"
  ## Used for TLS client certificate authentication
  # tls_key = "/path/to/keyfile"
  ## Password for the key file if it is encrypted
  # tls_key_pwd = ""
  ## Send the specified TLS server name via SNI
  # tls_server_name = "kubernetes.example.com"
  ## Minimal TLS version to accept by the client
  # tls_min_version = "TLS12"
  ## List of ciphers to accept, by default all secure ciphers will be accepted
  ## See https://pkg.go.dev/crypto/tls#pkg-constants for supported values.
  ## Use "all", "secure" and "insecure" to add all support ciphers, secure
  ## suites or insecure suites respectively.
  # tls_cipher_suites = ["secure"]
  ## Renegotiation method, "never", "once" or "freely"
  # tls_renegotiation_method = "never"
  ## Use TLS but skip chain & host verification
  # insecure_skip_verify = false

Databricks

[[outputs.http]]
  ## Databricks SQL Statement Execution API endpoint
  url = "https://{{ env "DATABRICKS_HOST" }}/api/2.0/sql/statements"

  ## Use POST to submit each Telegraf batch as a SQL request
  method = "POST"

  ## Personal-access token (PAT) for workspace or service principal
  headers = { Authorization = "Bearer {{ env "DATABRICKS_TOKEN" }}" }

  ## Send JSON that wraps the metrics batch in a SQL INSERT (or PUT into a Volume)
  content_type = "application/json"

  ## Serialize metrics as JSON so they can be embedded in the SQL statement
  data_format = "json"
  json_timestamp_units = "1ms"

  ## Build the request body.  Telegraf replaces the template variables at runtime.
  ## Example inserts a row per metric into a Unity-Catalog table.
  body_template = """
  {
    \"statement\": \"INSERT INTO ${TARGET_TABLE} VALUES {{range .Metrics}}(from_unixtime({{.timestamp}}/1000), {{.fields.usage}}, '{{.tags.host}}'){{end}}\",
    \"warehouse_id\": \"${WAREHOUSE_ID}\"
  }
  """

  ## Optional: add batching limits or TLS settings
  # batch_size = 500
  # timeout     = "10s"

Input and output integration examples

LDAP

1. Monitoring Directory Performance: Use the LDAP Telegraf plugin to continuously track and analyze the number of operations completed, initiated connections, and server response times. By visualizing this data over time, administrators can identify performance bottlenecks in directory services, enabling proactive optimization.

2. Alerting on Security Events: Integrate the plugin with an alerting system to notify administrators when certain metrics, such as bind_security_errors or unauth_binds, exceed predefined thresholds. This setup can enhance security monitoring by providing real-time insights into potential unauthorized access attempts.

3. Capacity Planning: Leverage the metrics collected by the LDAP plugin to perform capacity planning. Analyze connection trends, maximum threads in use, and operational statistics to forecast future resource needs, ensuring the LDAP server can handle expected peak loads without degrading performance.

4. Compliance and Auditing: Use the operational metrics obtained via this plugin to assist in compliance audits. By regularly checking metrics like anonymous_binds and security_errors, organizations can ensure that their directory services adhere to security policies and regulatory requirements.

Databricks

1. Edge-to-Lakehouse Telemetry Pipe: Deploy Telegraf on factory PLCs to sample vibration metrics and post them every second to a serverless SQL warehouse. Delta tables power PowerBI dashboards that alert engineers when thresholds drift.
2. Blue-Green CI/CD Rollout Metrics: Attach a Telegraf sidecar to each Kubernetes canary pod; it inserts container stats into a Unity Catalog table tagged by deployment_id, letting Databricks SQL compare error-rate percentiles and auto-rollback underperforming versions.
3. SaaS Usage Metering: Insert per-tenant API-call counters via the HTTP plugin; a nightly Lakehouse query aggregates usage into invoices, eliminating custom metering micro-services.
4. Security Forensics Lake: Upload JSON batches of Suricata IDS events to a Unity Catalog volume using PUT commands, then run COPY INTO for near-real-time enrichment with Delta Live Tables, producing a searchable threat-intel lake that joins network logs with user session data.

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